Senior Data Science Manager — AI & Analytics Leader

Visa

Foster City (CA)

On-site

USD 192,000 - 308,000

Full time

14 days+

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Benefits offered by this job

Medical

Dental

Vision

401(k)

FSA/HSA

Life Insurance

Paid Time Off

Wellness Program

Job summary

Visa is seeking a Senior Manager for Global Data and AI within the Consumer Payments Strategic Analytics team. You will deliver scalable analytics, AI‑driven insights, and data science solutions to inform product strategy and drive innovation across internal Visa business teams.

You’ll act as an internal data consultant, translating business questions into analytical approaches, extracting insights from large Visa data, and building repeatable tools and models for data‑driven decisions.

Qualifications

8+ years of relevant work experience with a Bachelor’s Degree or at least 5 years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 11+ years of relevant work experience.
9+ years of relevant experience with a Bachelor Degree or 7+ years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 3+ years of experience with a PhD
6+ years of analytics, data science, or related experience with a Bachelor’s degree; 4+ years with a Master’s/MBA; or 3+ years with a PhD
Excellent programming skills in Python and SQL, with hands‑on experience using large‑scale data environments such as Spark, Hive, Hadoop, or similar platforms.
Experience with AI/ML, including generative AI, LLMs, and AI‑assisted development tools such as Claude Code, GitHub Copilot, OpenAI Codex, or similar platforms.
Experience with agentic AI concepts and frameworks such as LangGraph, LangChain, or comparable orchestration tools.
Ability to build predictive and descriptive models, perform data mining and statistical modeling, and apply appropriate analytical methodologies to solve business problems.
Experience developing visual insights, dashboards, and reporting solutions using tools such as Tableau, Power BI, or similar business intelligence platforms.
Strong ability to translate data‑driven analysis into business impact, including excellent storytelling, presentation, and executive communication skills.
Proven track record of delivering analytics projects from scoping through execution, quality control, stakeholder communication, and implementation support.
Strong stakeholder management skills and demonstrated ability to work effectively with internal business teams in cross‑functional and global environments.
Deep understanding of Visa’s business, consumer payments, issuer and merchant analytics, and the broader payments ecosystem.
Experience with cloud‑based data platforms, MLOps practices, model deployment, and reusable analytical frameworks.
Prior experience in financial services, payments, fintech, consulting, or internal business analytics is a plus.
Highly organized, results‑oriented, and comfortable working in a fast‑paced environment with multiple priorities and internal stakeholders.

Responsibilities

Lead, execute, and deliver data science and strategic analytics projects that support Consumer Payments priorities.
Partner with internal teams to define project scope, methodologies, and deliver actionable solutions.
Analyze large datasets using Python, SQL, Spark, Hive, R to generate insights and build models.
Develop predictive, descriptive, and diagnostic analytics, including segmentation and forecasting.
Build scalable dashboards and self‑service analytics for data democratization.
Apply AI/ML and generative AI techniques to improve analytics delivery and automate workflows.
Design, prototype, and deploy analytical models with data engineering and platform teams.
Maintain quality control, documentation, and reproducibility across deliverables.
Translate complex analyses into executive-ready insights and recommendations.
Identify market, product, customer, and portfolio trends through deep analysis of payments data.
Collaborate to embed data and AI into decision‑making across functions.
Contribute to thought leadership with reusable frameworks and innovative analytic approaches.